fse-baro 0.0.7

BARO: Robust Root Cause Analysis for Microservices via Multivariate Bayesian Online Change Point Detection

BARO: Robust Root Cause Analysis for Microservices via Multivariate Bayesian Online Change Point Detection

In the progress of preparing the Artifact submission:

• Deadline 30/4
• https://2024.esec-fse.org/track/fse-2024-artifacts#submission-for-replicated-and-reproduced-badges
• Can apply for all three badges: Available, Function, Reusable

**Functional: The artifacts associated with the research are found to be documented, consistent, complete, exercisable, and include appropriate evidence of verification and validation. **

**Reusable: Functional + the artifacts associated with the paper are of a quality that significantly exceeds minimal functionality. They are very carefully documented and well-structured to the extent that reuse and repurposing is facilitated. In particular, norms and standards of the research community for artifacts of this type are strictly adhered to. **

TODO:

• reproduce RobustScorer
• reproduce BOCPD
• make docs directly on readme, show available AD + RCA methods + tutorials
• make the API
• restructure + lint code
• better readme
• make a Helm chart

Installation

Install from PyPI

pip install fse-baro

Or, build from source

git clone https://github.com/phamquiluan/baro.git && cd baro
pip install -e .

How-to-use

from baro import BARO

m = BARO()

anomalies = m.detect_anomalies(data)
root_causes = m.rca(data, anomalies=anomalies)
print(root_causes)

Download Paper

TBD

Download Datasets

TBD

Performance comparison

TBD

Citation

@inproceedings{pham2024baro,
  title={BARO: Robust Root Cause Analysis for Microservices via Multivariate Bayesian Online Change Point Detection},
  author={Luan Pham, Huong Ha, and Hongyu Zhang},
  booktitle={Proceedings of the 32nd ACM Symposium on the Foundations of Software Engineering (FSE'24)},
  year={2024},
  organization={ACM}
}